U.S. patent number 10,337,973 [Application Number 15/358,156] was granted by the patent office on 2019-07-02 for method and system for estimating empirical snow depth.
This patent grant is currently assigned to Korea Meteorological Administration. The grantee listed for this patent is Korea Meteorological Administration. Invention is credited to Ki-Ho Chang, Jiwon Choi, Jin-Yim Jeong, Eunsil Jung, Baek-Jo Kim.
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United States Patent |
10,337,973 |
Choi , et al. |
July 2, 2019 |
Method and system for estimating empirical snow depth
Abstract
Disclosed is a system for estimating a snow depth including: an
optical disdrometer for acquiring information on diameters of snow
particles and particle number concentration; a laser snow depth
gauge for measuring the height of snow accumulated through a laser
beam type sensor to provide an observed stop depth; an estimated
snow depth equation calculator for determining an optimal index for
the diameters of the snow particles provided by the optical
disdrometer, substituting the optimal index for a snow depth
calculation equation as a first mathematical equation to calculate
a computed snow depth, obtaining correlation between the observed
snow depth and the computed snow depth, and calculating a
regression equation between the observed snow depth and the
computed snow depth as an estimated snow depth equation; and a snow
depth estimator for estimating the snow depth on the basis of the
estimated snow depth equation, and the first mathematical
equation.
Inventors: |
Choi; Jiwon (Seogwipo-si,
KR), Chang; Ki-Ho (Seogwipo-si, KR), Jung;
Eunsil (Seogwipo-si, KR), Jeong; Jin-Yim (Seoul,
KR), Kim; Baek-Jo (Jeju-si, KR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Korea Meteorological Administration |
Seoul |
N/A |
KR |
|
|
Assignee: |
Korea Meteorological
Administration (Seoul, KR)
|
Family
ID: |
57460316 |
Appl.
No.: |
15/358,156 |
Filed: |
November 22, 2016 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180136105 A1 |
May 17, 2018 |
|
Foreign Application Priority Data
|
|
|
|
|
Nov 17, 2016 [KR] |
|
|
10-2016-0153057 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01B
11/08 (20130101); G01N 15/06 (20130101); G01N
15/0205 (20130101); G01W 1/14 (20130101); G01N
15/1459 (20130101); G01B 11/22 (20130101); G01N
2015/0693 (20130101); G01N 2015/1075 (20130101); G01N
2015/0046 (20130101); G01N 2015/1493 (20130101) |
Current International
Class: |
G01N
15/02 (20060101); G01B 11/08 (20060101); G01N
15/06 (20060101); G01B 11/22 (20060101); G01W
1/14 (20060101); G01N 15/14 (20060101); G01N
15/00 (20060101); G01N 15/10 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
|
|
|
|
|
|
|
10-1487745 |
|
Jan 2015 |
|
KR |
|
10-1509108 |
|
Apr 2015 |
|
KR |
|
10-1530261 |
|
Jun 2015 |
|
KR |
|
10-1538858 |
|
Jul 2015 |
|
KR |
|
Primary Examiner: Breene; John E
Assistant Examiner: Morgan; Jeffrey C
Attorney, Agent or Firm: Revolution IP, PLLC
Claims
The invention claimed is:
1. A system for estimating a snow depth comprising: an optical
disdrometer for acquiring information on diameters of snow
particles and particle number concentration; a laser snow depth
gauge for measuring the height of snow accumulated through a laser
beam type sensor to provide an observed snow depth; an estimated
snow depth equation calculator for determining an optimal index for
the diameters of the snow particles provided by the optical
disdrometer, substituting the optimal index for a snow depth
calculation equation as a first mathematical equation as indicated
below to calculate a computed snow depth, obtaining correlation
between the observed snow depth and the computed snow depth, and
calculating a regression equation between the observed snow depth
and the computed snow depth as an estimated snow depth equation;
and a snow depth estimator for estimating the snow depth on the
basis of the estimated snow depth equation, and the first
mathematical equation,
.intg..times..times..function..times..times..times..function..times..DELT-
A..times..times. ##EQU00005## wherein M.sub.n indicates a computed
snow depth, D.sub.max a maximum diameter size, D.sub.min a minimum
diameter size, D a diameter per volume, n an index for diameter,
and N(D) a particle number concentration by diameter size.
2. The system according to claim 1, further comprising an automatic
weather system for periodically producing weather observation
information.
3. The system according to claim 2, wherein the estimated snow
depth equation calculator comprises: an information acquiring unit
for receiving the observed snow depth from the laser snow depth
gauge and acquiring the weather information from the automatic
weather system; an optimal index determining unit for computing
R.sup.2 between the computed result value from the first
mathematical equation and the observed snow depth and determining,
as the optimal index, the index at which the R.sup.2 has the
highest value; a computed snow depth calculating unit for applying
the determined index to the snow depth calculation equation as the
first mathematical equation to calculate the computed snow depth; a
correlation calculating unit for classifying the computed snow
depth and the observed snow depth by snow accumulation time and
calculating the correlation between the computed snow depth and the
observed snow depth by snow accumulation time; an estimated snow
depth equation producing unit for determining a second mathematical
equation as the regression equation between the computed snow depth
and the observed snow depth by snow accumulation time as the
estimated snow depth equation, and the second mathematical
equation, Estimated Snow Depth (ESD)=A.times.M.sub.2.36 wherein
M.sub.2.36 is an optimal index obtained through the first
mathematical equation, A is an inclination of the regression
equation by snow accumulation time, and if the accumulation time is
under three hours, A is 0.33, while A is 0.35 if the accumulation
time is more than five hours.
4. A method for estimating a snow depth by a system comprising an
optical disdrometer, a laser snow depth gauge, an estimated snow
depth equation calculator, and a snow depth estimator, the
estimated snow depth equation calculator being connected to the
optical disdrometer, the laser snow depth gauge, and the snow depth
estimator respectively, the method comprising the steps of:
acquiring, by the optical disdrometer, information on diameters of
snow particles and particle number concentration and, by the laser
snow depth gauge, an observed snow depth; determining, by the
estimated snow depth equation calculator, an optimal index for the
diameters of the snow particles provided by the optical
disdrometer; substituting, by the estimated snow depth equation
calculator, the optimal index for a snow depth calculation equation
as a third mathematical equation to calculate a computed snow depth
and obtaining correlation between the observed snow depth and the
computed snow depth; calculating, by the estimated snow depth
equation calculator, a regression equation between the observed
snow depth and the computed snow depth as an estimated snow depth
equation; and estimating, by the snow depth estimator, the snow
depth on the basis of the estimated snow depth equation, and the
third mathematical equation,
.intg..times..times..function..times..times..times..function..times..DELT-
A..times..times. ##EQU00006## wherein M.sub.n indicates a computed
snow depth, D.sub.max a maximum diameter size, D.sub.min a minimum
diameter size, D a diameter per volume, n an index for diameter,
and N(D) a particle number concentration by diameter size.
5. The method according to claim 4, wherein the step of determining
the optimal index for the diameters of the snow particles comprises
the steps of computing R.sup.2 between the computed result value
from the third mathematical equation and the observed snow depth
and determining, as the optimal index, the index at which the
R.sup.2 has the highest value.
6. The method according to claim 4, wherein the estimated snow
depth equation is expressed by a fourth mathematical equation, and
the fourth mathematical equation, Estimated Snow Depth
(ESD)=A.times.M.sub.2.36 wherein M.sub.2.36 is an optimal index
obtained through the first mathematical equation, A is an
inclination of the regression equation by snow accumulation time,
and if the accumulation time is under three hours, A is 0.33, while
A is 0.35 if the accumulation time is more than five hours.
Description
CROSS REFERENCE TO PRIOR APPLICATIONS
This application claims priority under 35 U.S.C. .sctn. 119(a) to
Korean Patent Application No. 10-2016-0153057 filed on Nov. 17,
2016, the entire content of which is incorporated herein by
reference.
BACKGROUND
The present invention relates to a method and system for estimating
a snow depth and, more particularly, to a method and system for
estimating an empirical snow depth that makes use of snow particle
size distributions.
An observation network is built on a cloud physics observation
system of Daegwallyeong in Korea so as to verify snow enhancement
experiments, and in this case, a variety of measuring instruments
are operated in the observation network. A general object of the
snow enhancement is to increase the quantity of snow accumulated on
the ground, and the quantity of snow increased by the snow
enhancement experiments has been checked in Korea through various
observation instruments. Particularly, snow produced by fine
particles is accumulated on the ground, which is observed generally
by a snow depth gauge. The snow depth gauge serves to measure the
snow depth accumulated on a snow measuring plate. Conventional
technologies for measuring the snow depth accumulated on the snow
measuring plate have been proposed, for example, in Korean Patent
No. 10-1530261 entitled "snow depth measuring device and method",
Korean Patent No. 10-1509108 entitled "snow depth measuring device
and method for operating the same", and Korean Patent No.
10-1538858 entitled "snow depth measuring device".
If the snow depth accumulated on the snow measuring plate is
measured, observation errors may occur due to various environmental
influences. Since snow is weak in heat, especially, it may melt
under the influence of the ground temperature transmitted to the
snow measuring plate before accumulated on the ground, and further,
snow may fall down due to the influence of wind, thereby causing
the observation errors. In addition to the weather factors,
besides, the observation errors may occur by animals or plants
around the snow measuring plate. Due to the influences of such
various factors as mentioned above, accordingly, it is hard to
accurately observe the snow depth through the snow depth gauge.
Another method and system for measuring a snow depth is proposed
wherein a light emitting element and a light receiving element are
accommodated in a scale ruler so that the light emitted from the
light emitting element is absorbed to the light receiving element,
thereby measuring the snow depth. Such conventional method and
system is disclosed, for example, in Korean Patent No. 10-1487745
entitled influence "snow depth measuring device using light source
and camera".
Furthermore, a variety of methods and studies have been proposed to
observe a snow depth in a more accurate manner. In many countries,
accordingly, theoretical equations and empirical equations for
estimating a snow depth have been calculated. Since such equations
need density observation values, however, they are complicated in
computing. So as to remove such complication, accordingly, there is
a need to calculate the empirical equation in a simpler manner.
Therefore, there is a definite need for development of a method and
system for estimating a snow depth, which is capable of solving the
above-mentioned problems, needing no density observation value, and
reducing the complication in computing.
SUMMARY
Accordingly, the present invention has been made in view of the
above-mentioned problems occurring in the prior art, and it is an
object of the present invention to provide a method and system for
estimating an empirical snow depth that makes use of snow particle
size distributions, while no density observation value is being
needed and the complication in computing is being reduced.
To accomplish the above-mentioned object, according to a first
aspect of the present invention, there is provided a system for
estimating a snow depth including: an optical disdrometer for
acquiring information on diameters of snow particles and particle
number concentration; a laser snow depth gauge for measuring the
height of snow accumulated through a laser beam type sensor to
provide an observed snow depth; an estimated snow depth equation
calculator for determining an optimal index for the diameters of
the snow particles provided by the optical disdrometer,
substituting the optimal index for a snow depth calculation
equation as a first mathematical equation as indicated below to
calculate a computed snow depth, obtaining correlation between the
observed snow depth and the computed snow depth, and calculating a
regression equation between the observed snow depth and the
computed snow depth as an estimated snow depth equation; and a snow
depth estimator for estimating the snow depth on the basis of the
estimated snow depth equation, and the first mathematical
equation,
.intg..times..times..function..times..times..times..function..times..DELT-
A..times..times. ##EQU00001##
wherein M.sub.n indicates a computed snow depth, D.sub.max a
maximum diameter size, D.sub.min a minimum diameter size, D a
diameter per volume, n an index for diameter, and N(D) a particle
number concentration by diameter size.
According to the present invention, desirably, the system further
includes an automatic weather system for periodically producing
weather observation information.
According to the present invention, desirably, the estimated snow
depth equation calculator includes: an information acquiring unit
for receiving the observed snow depth from the laser snow depth
gauge and acquiring the weather information from the automatic
weather system; an optimal index determining unit for computing
R.sup.2 between the computed result value from the first
mathematical equation and the observed snow depth and determining,
as the optimal index, the index at which the R.sup.2 has the
highest value; a computed snow depth calculating unit for applying
the determined index to the snow depth calculation equation as the
first mathematical equation to calculate the computed snow depth; a
correlation calculating unit for classifying the computed snow
depth and the observed snow depth by snow accumulation time and
calculating the correlation between the computed snow depth and the
observed snow depth by snow accumulation time; an estimated snow
depth equation producing unit for determining a second mathematical
equation as the regression equation between the computed snow depth
and the observed snow depth by snow accumulation time as the
estimated snow depth equation, and the second mathematical
equation, Estimated Snow Depth (ESD)=A.times.M.sub.2.36
wherein M.sub.2.36 is an optimal index obtained through the first
mathematical equation, A is an inclination of the regression
equation by snow accumulation time, and if the accumulation time is
under three hours, A is 0.33, while A is 0.35 if the accumulation
time is more than five hours.
To accomplish the above-mentioned object, according to a second
aspect of the present invention, there is provided a method for
estimating a snow depth including the steps of: acquiring
information on diameters of snow particles and particle number
concentration; determining an optimal index for the diameters of
the snow particles; substituting the optimal index for a snow depth
calculation equation as a third mathematical equation as indicated
below to calculate a computed snow depth and obtaining correlation
between the observed snow depth and the computed snow depth;
calculating a regression equation between the observed snow depth
and the computed snow depth as an estimated snow depth equation;
and estimating the snow depth on the basis of the estimated snow
depth equation, and the third mathematical equation,
.intg..times..times..function..times..times..times..function..times..DELT-
A..times..times. ##EQU00002##
wherein M.sub.n indicates a computed snow depth, D.sub.max a
maximum diameter size, D.sub.min a minimum diameter size, D a
diameter per volume, n an index for diameter, and N(D) a particle
number concentration by diameter size.
According to the present invention, desirably, the step of
determining the optimal index for the diameters of the snow
particles includes the steps of computing R.sup.2 between the
computed result value from the third mathematical equation and the
observed snow depth and determining, as the optimal index, the
index at which the R.sup.2 has the highest value.
According to the present invention, desirably, the estimated snow
depth equation is expressed by a fourth mathematical equation, and
the fourth mathematical equation, Estimated Snow Depth
(ESD)=A.times.M.sub.2.36
wherein M.sub.2.36 is an optimal index obtained through the first
mathematical equation, A is an inclination of the regression
equation by snow accumulation time, and if the accumulation time is
under three hours, A is 0.33, while A is 0.35 if the accumulation
time is more than five hours.
According to the present invention, the method and system for
estimating an empirical snow depth makes use of snow particle size
distribution, while no density observation value is being needed
and the complication in computing is being reduced. Accordingly,
the method and system for estimating an empirical snow depth
according to the present invention can calculate a snow depth from
more accurate measurements of the optical disdrometer, determine
the estimated snow depth equation optimized for a snow depth case,
and thus estimate the snow depth on the basis of the estimated snow
depth equation.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram showing a system for estimating a snow
depth according to the present invention.
FIG. 2 is a block diagram showing an estimated snow depth equation
calculator of the system according to the present invention.
FIG. 3 is a graph showing an R.sup.2 between M.sub.n and snow depth
according to the changes in diameters of snow particles.
FIG. 4 illustrates graphs (a), (b), (c) and (d) showing computed
snow depths and observed snow depths by snow accumulation time.
FIG. 5 is a flow chart showing a method for estimating a snow depth
according to the present invention.
DETAILED DESCRIPTION
Before the present invention is disclosed and described, it is to
be understood that the disclosed embodiments are merely exemplary
of the invention, which can be embodied in various forms.
Therefore, specific structural and functional details disclosed
herein are not to be interpreted as limiting, but merely as a basis
for the claims and as a representative basis for teaching one of
ordinary skill in the art to variously employ the present invention
in virtually any appropriately detailed structure.
Hereinafter, an explanation on a method and system for estimating
an empirical snow depth according to the present invention will be
in detail given with reference to the attached drawing. In the
description, the same reference numerals in the drawing will be
used to describe the same components. If it is determined that the
detailed explanation on the well known technology related to the
present invention makes the scope of the present invention not
clear, the explanation will be avoided for the brevity of the
description.
According to the present invention, an empirical equation capable
of inducing a snow depth is calculated using an instrument like a
disdrometer. The disdrometer measures particle sizes and falling
speeds of rain drops or snow during precipitation and thus
calculates LWC (Liquid Water Content), rain rate, and reflectivity
using the measurements. Unlike water drops (rain drops), ice
particles (snow) have a variety of shapes and densities, and so as
to estimate a snow depth, accordingly, there is a need to induce a
relational equation adequate for the snow depth.
According to the present invention, a PARSIVEL (Particle Size
Velocity) disdrometer is provided to acquire information in an
optical manner when rain or snow falls during precipitation.
According to the present invention, in more detail, the information
on the snow particle diameters and water densities measured through
the PARSIVEL disdrometer is utilized to calculate a computed snow
depth as a result value of an optimal moment equation for the
diameters, and correlation between the computed snow depth and the
observed snow depth through a snow depth gauge is obtained to
calculate an empirical equation, so that snow depths can be
estimated later with the information of the PARSIVEL disdrometer,
not with the information of the snow depth gauge.
A system 100 for estimating an empirical snow depth according to
the present invention is shown in FIG. 1.
FIG. 1 is a block diagram showing the system 100 for estimating a
snow depth according to the present invention.
According to the present invention, the system 100 for estimating a
snow depth includes an optical disdrometer 110, a laser snow depth
gauge 120, an automatic weather system 130, an estimated snow depth
equation calculator 140, and a snow depth estimator 150.
The optical disdrometer 110 acquires the information (for example,
shape) on a falling object using a laser beam. For example, the
optical disdrometer 110 emits a laser beam (of 650 nm), converts
the intensities of light cut-off strength by the particles of the
falling object into an electrical signal, measures the particle
size and fall velocity, and calculates, with the reflectivity
induced from the measurements, snow strength, snow particle size
(diameter), particle number concentration, fall velocity, distance
of vision, snow shape, liquid water content, and the like.
Desirably, the optical disdrometer 110 is a PARSIVEL
disdrometer.
The laser snow depth gauge 120 automatically measures the height of
snow accumulated through a laser beam type sensor. That is, the
laser snow depth gauge 120 senses the surface of snow accumulated
from the floor on which snow is not accumulated through the sensor
and thus measures the snow depth.
The automatic weather system AWS 130 serves to allow weather
observation to be automatically carried out through a computer. In
more detail, the automatic weather system 130, which automatically
produces weather observation information periodically, is installed
on a place to which people do not come near or there is no weather
station to acquire weather information. The weather information
includes a wind direction, wind speed, atmospheric pressure,
humidity, temperature, and amount of precipitation.
The estimated snow depth equation calculator 140 determines an
optimal index for the diameters of the snow particles using the
diameters of the snow particles calculated by the optical
disdrometer 110, obtains correlation between the computed snow
depth calculated from the optimal index and the observed snow depth
from the laser snow depth gauge 120, calculates an estimated snow
depth equation, and thus expects an accurate snow depth.
The snow depth estimator 150 estimates the snow depth from the
estimated snow depth equation produced by the estimated snow depth
equation calculator 140.
FIG. 2 is a block diagram showing the estimated snow depth equation
calculator of the system according to the present invention.
In more detail, the estimated snow depth equation calculator 140
includes an information acquiring unit 141, an optimal index
determining unit 142, a computed snow depth calculating unit 143, a
correlation calculating unit 144, and an estimated snow depth
equation producing unit 145.
First, the information acquiring unit 141 receives the weather
information from the automatic weather system 130. The weather
information includes wind speed information and air temperature
information. Further, the information acquiring unit 141 determines
whether a snow depth calculation condition is satisfied or not. In
more detail, the information acquiring unit 141 selects the
materials by time in which a snow depth is actually observed from
days having a daily snow depth of more than 1 cm through the laser
snow depth gauge 120. Further, the information acquiring unit 141
selects the days having the wind speed of 5 m/s or under and the
air temperature of 0.degree. C. or under from the selected
materials so as to reduce the observation error and choose
appropriate cases.
That is, the information acquiring unit 141 selects the days having
a daily snow depth of more than 1 cm from the materials of the
laser snow depth gauge 120, and from the selected days, if the wind
speed is 5 m/s or under and the air temperature is 0.degree. C. or
under, the information acquiring unit 141 determines that the snow
depth calculation condition is satisfied. The snow depth
calculation condition is the condition in which the particle
observation error of the optical disdrometer 110 is low.
The optimal index determining unit 142 extracts the optimal index
for the diameters of the snow particles. So as to extract the
optimal index, accordingly, the optimal index determining unit 142
applies the values of indexes n for the diameters to 0 to 10 at
intervals of 0.01 in a moment equation as a first mathematical
equation as will be discussed below and calculates R.sup.2 between
the computed snow depth and the observed snow depth.
.times..times..times..times..times. ##EQU00003##
.intg..times..times..function..times..times..times..function..times..DELT-
A..times..times. ##EQU00003.2##
In the first mathematical equation, M.sub.n indicates a computed
snow depth, D.sub.max a maximum diameter size, D.sub.min a minimum
diameter size, D a diameter per volume, n an index for diameter,
and N(D) a particle number concentration by diameter size.
The optimal index determining unit 142 computes R.sup.2 between the
computed result value from the first mathematical equation and the
observed snow depth and determines, as an optimal index, the index
at which the R.sup.2 has the highest value.
FIG. 3 is a graph showing the R.sup.2 between M.sub.n and snow
depth according to the changes in diameters n of snow
particles.
Referring to FIG. 3, when the index n is 2.34, R.sup.2 has an
optimal value. In this case, the optimal index is determined as
2.34.
The computed snow depth calculating unit 143 obtains a snow depth
calculation equation using the determined optimal index. For
example, if the optimal index is M.sub.2.36, it is substituted for
the moment equation as the first mathematical equation, thereby
obtaining the snow depth calculation equation like a second
mathematical equation as will be described below.
.times..times..times..times..times. ##EQU00004##
.times..times..times..function..times..DELTA..times..times.
##EQU00004.2##
The computed snow depth calculating unit 143 calculates the
computed snow depth from the snow depth calculation equation as
indicated in the second mathematical equation.
The correlation calculating unit 144 classifies the computed snow
depth calculated from the snow depth calculation equation as
indicated in the second mathematical equation and the observed snow
depth by snow accumulation time, calculates the correlation between
the computed snow depth and the observed snow depth by snow
accumulation time, and checks the correlation therebetween.
FIG. 4 illustrates graphs (a), (b), (c) and (d) showing the
computed snow depths and the observed snow depths by snow
accumulation time.
Referring to FIG. 4, the graph (a) shows the scatter plot between
the computed snow depth and the observed snow depth in case of one
hour accumulation of snowfall, the graph (b) shows the scatter plot
therebetween in case of three hour accumulation of snowfall, the
graph (c) shows the scatter plot therebetween in case of one day
accumulation of snowfall, and the graph (d) shows the scatter plot
therebetween in case of the accumulation during the lasting time of
snowfall.
The estimated snow depth equation producing unit 145 determines a
regression equation between the computed snow depth and the
observed snow depth by snow accumulation time as an estimated snow
depth ESD equation as a third mathematical equation. Estimated Snow
Depth (ESD)=A.times.M.sub.2.36 [Third mathematical equation]
In the third mathematical equation, M.sub.2.36 is an optimal index
obtained through the first mathematical equation, and A is an
inclination of the regression equation by snow accumulation time,
wherein if the snow accumulation time is under three hours, A is
0.33, and if the snow accumulation time is more than five hours, A
is 0.35.
If such estimated snow depth equation is used, a snow depth can be
accurately expected.
For example, the snow depth estimator 150 applies the computed snow
depth through the moment equation as the first mathematical
equation to which the optimal index is applied to the estimated
snow depth equation produced by the estimated snow depth equation
calculator 140 to the ESD equation as the third mathematical
equation and thus estimates a snow depth.
The estimated snow depth equation allows the snow depth to be
estimated later, not on the basis of the laser snow depth gauge,
but on the basis of the optical disdrometer. That is, the snow
depth can be estimated from the estimated snow depth equation on
the basis of the information obtained by the optical disdrometer,
instead of the observed snow depth obtained from the laser snow
depth gauge.
FIG. 5 is a flow chart showing a method for estimating a snow depth
according to the present invention.
Referring to FIG. 5, the estimated snow depth equation calculator
140 determines whether the snow depth calculation condition is
satisfied at step 210. In this case, the estimated snow depth
equation calculator 140 selects the days having a daily snow depth
of more than 1 cm through the laser snow depth gauge 120. Further,
the estimated snow depth equation calculator 140 receives the
weather information from the automatic weather system 130. The
weather information includes wind speed information and air
temperature information. The estimated snow depth equation
calculator 140 determines whether the wind speed is under 5 m/s and
the air temperature is under 0.degree. C. If the wind speed is
under 5 m/s and the air temperature is under 0.degree. C. among the
days having a daily snow depth of more than 1 cm through the laser
snow depth gauge 120, the estimated snow depth equation calculator
140 determines that the snow depth calculation condition is
satisfied. The snow depth calculation condition is the condition in
which the particle observation error of the optical disdrometer 110
is low.
If the snow depth calculation condition is satisfied, the estimated
snow depth equation calculator 140 determines the optimal index
according to the diameters of the snow particles at step 220. In
more detail, the estimated snow depth equation calculator 140
applies the values of indexes n for the diameters to 0 to 10 at
intervals of 0.01 in the moment equation as the first mathematical
equation and calculates the R.sup.2 between the computed estimation
value and the observed snow depth.
The estimated snow depth equation calculator 140 computes the
R.sup.2 between the computed result value from the first
mathematical equation and the observed snow depth, and thus
determines, as an optimal index, the index at which the R.sup.2 has
the highest value.
Next, the estimated snow depth equation calculator 140 applies the
determined index to the snow depth calculation equation as
indicated by the second mathematical equation at step 230 and
calculates the computed snow depth.
Further, the estimated snow depth equation calculator 140 acquires
the observed snow depth from, for example, the laser snow depth
gauge 120, at step 240.
The estimated snow depth equation calculator 140 at step 250
classifies the computed snow depth calculated from the snow depth
calculation equation as indicated in the second mathematical
equation and the observed snow depth by snow accumulation time.
Next, at step 260, the estimated snow depth equation calculator 140
calculates the correlation between the computed snow depth and the
observed snow depth by snow accumulation time and checks the
correlation therebetween.
Next, as step 270, the estimated snow depth equation calculator 140
determines a regression equation between the computed snow depth
and the observed snow depth by snow accumulation time as the
estimated snow depth ESD equation as the third mathematical
equation.
If the estimated snow depth equation is used, the snow depth can be
accurately expected. In other words, the snow depth can be
estimated from the estimated snow depth equation on the basis of
the information obtained by the optical disdrometer, instead of the
observed snow depth obtained from the laser snow depth gauge.
According to the above-mentioned embodiment of the present
invention, for example, snowfall is proposed, but the present
invention is not limited thereto. For example, the present
invention may be applied in case of rainfall, which is obvious to a
person skilled in the art.
Since errors may occur by the above-mentioned characteristics of
the snow depth gauge, the system and method for estimating the snow
depth according to the present invention utilizes the more accurate
measurements of the optical disdrometer and calculates the snow
depth. Further, the system and method for estimating the snow depth
according to the present invention determines the relational
equation optimized for the snow depth cases to calculate the
estimated snow depth equation with the observed snow depth through
the snow depth gauge and thus estimates the snow depth on the basis
of the estimated snow depth equation.
According to the present invention, the method for estimating the
snow depth has a form of program commands carried out through
various computer means in such a manner as to be recorded in
computer readable media. The computer readable media include
program commands, data files, or data structures solely or
combinedly. The program commands recorded in the media are
specially designed and built for the embodiment of the present
invention, and otherwise, they may be known to a person skilled in
computer software and usable by the person. Examples of the
computer readable media include hardware devices configured
specially to store and carry out the program commands, such as
magnetic media like hard disks, floppy disks and magnetic tapes,
optical media like CD-ROM and DVD, magneto-optical media like
floptical disks, ROM, RAM, and a flash memory. Examples of the
program command include machine codes made by compilers and
high-level language codes carried out by a computer through an
interpreter. The hardware devices are operated by one or more
software modules so as to carry out the operations of the
embodiment of the present invention, and vice versa.
While the present invention has been described with reference to
the particular illustrative embodiments, it is not to be restricted
by the embodiments but only by the appended claims. It is to be
appreciated that those skilled in the art can change or modify the
embodiments without departing from the scope and spirit of the
present invention.
* * * * *